Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.7 KiB
Average record size in memory104.1 B

Variable types

Text1
Categorical4
Numeric8

Alerts

CTR is highly overall correlated with Clicks and 3 other fieldsHigh correlation
Clicks is highly overall correlated with CTR and 4 other fieldsHigh correlation
Conversion_Rate is highly overall correlated with ROIHigh correlation
Conversions is highly overall correlated with CTR and 4 other fieldsHigh correlation
Cost is highly overall correlated with CTR and 4 other fieldsHigh correlation
Impressions is highly overall correlated with Clicks and 3 other fieldsHigh correlation
ROI is highly overall correlated with Conversion_RateHigh correlation
Revenue is highly overall correlated with CTR and 4 other fieldsHigh correlation
Campaign_ID has unique values Unique
Impressions has unique values Unique
Revenue has unique values Unique
CTR has unique values Unique
ROI has unique values Unique

Reproduction

Analysis started2025-04-28 17:29:38.753087
Analysis finished2025-04-28 17:29:56.470427
Duration17.72 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Campaign_ID
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2025-04-28T22:59:57.071955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.001
Min length5

Characters and Unicode

Total characters5001
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowAD001
2nd rowAD002
3rd rowAD003
4th rowAD004
5th rowAD005
ValueCountFrequency (%)
ad009 1
 
0.1%
ad1000 1
 
0.1%
ad001 1
 
0.1%
ad002 1
 
0.1%
ad003 1
 
0.1%
ad004 1
 
0.1%
ad005 1
 
0.1%
ad006 1
 
0.1%
ad985 1
 
0.1%
ad986 1
 
0.1%
Other values (990) 990
99.0%
2025-04-28T22:59:57.976066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1000
20.0%
D 1000
20.0%
1 301
 
6.0%
0 300
 
6.0%
2 300
 
6.0%
3 300
 
6.0%
4 300
 
6.0%
5 300
 
6.0%
6 300
 
6.0%
7 300
 
6.0%
Other values (2) 600
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3001
60.0%
Uppercase Letter 2000
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 301
10.0%
0 300
10.0%
2 300
10.0%
3 300
10.0%
4 300
10.0%
5 300
10.0%
6 300
10.0%
7 300
10.0%
9 300
10.0%
8 300
10.0%
Uppercase Letter
ValueCountFrequency (%)
A 1000
50.0%
D 1000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3001
60.0%
Latin 2000
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 301
10.0%
0 300
10.0%
2 300
10.0%
3 300
10.0%
4 300
10.0%
5 300
10.0%
6 300
10.0%
7 300
10.0%
9 300
10.0%
8 300
10.0%
Latin
ValueCountFrequency (%)
A 1000
50.0%
D 1000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1000
20.0%
D 1000
20.0%
1 301
 
6.0%
0 300
 
6.0%
2 300
 
6.0%
3 300
 
6.0%
4 300
 
6.0%
5 300
 
6.0%
6 300
 
6.0%
7 300
 
6.0%
Other values (2) 600
12.0%

Campaign_Name
Categorical

Distinct30
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Originals Drop
 
44
NMD Edition
 
42
Predator Edition
 
41
NMD Restock
 
41
Stan Smith Collection
 
41
Other values (25)
791 

Length

Max length21
Median length17
Mean length15.346
Min length8

Characters and Unicode

Total characters15346
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYeezy Collection
2nd rowNMD Drop
3rd rowNMD Restock
4th rowStan Smith Restock
5th rowPredator Drop

Common Values

ValueCountFrequency (%)
Originals Drop 44
 
4.4%
NMD Edition 42
 
4.2%
Predator Edition 41
 
4.1%
NMD Restock 41
 
4.1%
Stan Smith Collection 41
 
4.1%
Ultraboost Launch 40
 
4.0%
Stan Smith Restock 39
 
3.9%
Yeezy Restock 38
 
3.8%
NMD Launch 38
 
3.8%
Predator Collection 36
 
3.6%
Other values (20) 600
60.0%

Length

2025-04-28T22:59:58.311312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
restock 216
10.0%
collection 208
9.6%
edition 199
9.2%
drop 189
8.7%
launch 188
8.7%
nmd 178
8.2%
originals 174
8.0%
predator 174
8.0%
smith 165
7.6%
stan 165
7.6%
Other values (2) 309
14.3%

Most occurring characters

ValueCountFrequency (%)
o 1510
 
9.8%
t 1443
 
9.4%
1165
 
7.6%
i 1119
 
7.3%
n 934
 
6.1%
e 900
 
5.9%
r 869
 
5.7%
a 859
 
5.6%
l 748
 
4.9%
c 612
 
4.0%
Other values (23) 5187
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11660
76.0%
Uppercase Letter 2521
 
16.4%
Space Separator 1165
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1510
13.0%
t 1443
12.4%
i 1119
9.6%
n 934
8.0%
e 900
7.7%
r 869
7.5%
a 859
7.4%
l 748
 
6.4%
c 612
 
5.2%
s 548
 
4.7%
Other values (10) 2118
18.2%
Uppercase Letter
ValueCountFrequency (%)
D 367
14.6%
S 330
13.1%
R 216
8.6%
C 208
8.3%
E 199
7.9%
L 188
7.5%
N 178
7.1%
M 178
7.1%
O 174
6.9%
P 174
6.9%
Other values (2) 309
12.3%
Space Separator
ValueCountFrequency (%)
1165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14181
92.4%
Common 1165
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1510
 
10.6%
t 1443
 
10.2%
i 1119
 
7.9%
n 934
 
6.6%
e 900
 
6.3%
r 869
 
6.1%
a 859
 
6.1%
l 748
 
5.3%
c 612
 
4.3%
s 548
 
3.9%
Other values (22) 4639
32.7%
Common
ValueCountFrequency (%)
1165
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1510
 
9.8%
t 1443
 
9.4%
1165
 
7.6%
i 1119
 
7.3%
n 934
 
6.1%
e 900
 
5.9%
r 869
 
5.7%
a 859
 
5.6%
l 748
 
4.9%
c 612
 
4.0%
Other values (23) 5187
33.8%

Product_Category
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Apparel
346 
Sneakers
327 
Accessories
327 

Length

Max length11
Median length8
Mean length8.635
Min length7

Characters and Unicode

Total characters8635
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSneakers
2nd rowAccessories
3rd rowSneakers
4th rowApparel
5th rowApparel

Common Values

ValueCountFrequency (%)
Apparel 346
34.6%
Sneakers 327
32.7%
Accessories 327
32.7%

Length

2025-04-28T22:59:58.540361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-28T22:59:58.798309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
apparel 346
34.6%
sneakers 327
32.7%
accessories 327
32.7%

Most occurring characters

ValueCountFrequency (%)
e 1654
19.2%
s 1308
15.1%
r 1000
11.6%
p 692
8.0%
A 673
7.8%
a 673
7.8%
c 654
 
7.6%
l 346
 
4.0%
S 327
 
3.8%
k 327
 
3.8%
Other values (3) 981
11.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7635
88.4%
Uppercase Letter 1000
 
11.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1654
21.7%
s 1308
17.1%
r 1000
13.1%
p 692
9.1%
a 673
8.8%
c 654
 
8.6%
l 346
 
4.5%
k 327
 
4.3%
n 327
 
4.3%
o 327
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
A 673
67.3%
S 327
32.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 8635
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1654
19.2%
s 1308
15.1%
r 1000
11.6%
p 692
8.0%
A 673
7.8%
a 673
7.8%
c 654
 
7.6%
l 346
 
4.0%
S 327
 
3.8%
k 327
 
3.8%
Other values (3) 981
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8635
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1654
19.2%
s 1308
15.1%
r 1000
11.6%
p 692
8.0%
A 673
7.8%
a 673
7.8%
c 654
 
7.6%
l 346
 
4.0%
S 327
 
3.8%
k 327
 
3.8%
Other values (3) 981
11.4%

Channel
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
TikTok
179 
Instagram
175 
YouTube
166 
Facebook
163 
Email
163 

Length

Max length10
Median length8
Mean length7.47
Min length5

Characters and Unicode

Total characters7470
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFacebook
2nd rowInstagram
3rd rowEmail
4th rowYouTube
5th rowYouTube

Common Values

ValueCountFrequency (%)
TikTok 179
17.9%
Instagram 175
17.5%
YouTube 166
16.6%
Facebook 163
16.3%
Email 163
16.3%
Google Ads 154
15.4%

Length

2025-04-28T22:59:59.082241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-28T22:59:59.304812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tiktok 179
15.5%
instagram 175
15.2%
youtube 166
14.4%
facebook 163
14.1%
email 163
14.1%
google 154
13.3%
ads 154
13.3%

Most occurring characters

ValueCountFrequency (%)
o 979
 
13.1%
a 676
 
9.0%
T 524
 
7.0%
k 521
 
7.0%
e 483
 
6.5%
i 342
 
4.6%
m 338
 
4.5%
u 332
 
4.4%
b 329
 
4.4%
g 329
 
4.4%
Other values (14) 2617
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5817
77.9%
Uppercase Letter 1499
 
20.1%
Space Separator 154
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 979
16.8%
a 676
11.6%
k 521
9.0%
e 483
8.3%
i 342
 
5.9%
m 338
 
5.8%
u 332
 
5.7%
b 329
 
5.7%
g 329
 
5.7%
s 329
 
5.7%
Other values (6) 1159
19.9%
Uppercase Letter
ValueCountFrequency (%)
T 524
35.0%
I 175
 
11.7%
Y 166
 
11.1%
F 163
 
10.9%
E 163
 
10.9%
G 154
 
10.3%
A 154
 
10.3%
Space Separator
ValueCountFrequency (%)
154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7316
97.9%
Common 154
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 979
 
13.4%
a 676
 
9.2%
T 524
 
7.2%
k 521
 
7.1%
e 483
 
6.6%
i 342
 
4.7%
m 338
 
4.6%
u 332
 
4.5%
b 329
 
4.5%
g 329
 
4.5%
Other values (13) 2463
33.7%
Common
ValueCountFrequency (%)
154
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 979
 
13.1%
a 676
 
9.0%
T 524
 
7.0%
k 521
 
7.0%
e 483
 
6.5%
i 342
 
4.6%
m 338
 
4.5%
u 332
 
4.4%
b 329
 
4.4%
g 329
 
4.4%
Other values (14) 2617
35.0%

Promo_Type
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Flash Sale
205 
New Launch
204 
Influencer Collab
202 
Limited Edition
199 
Seasonal Sale
190 

Length

Max length17
Median length15
Mean length12.979
Min length10

Characters and Unicode

Total characters12979
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeasonal Sale
2nd rowInfluencer Collab
3rd rowFlash Sale
4th rowSeasonal Sale
5th rowNew Launch

Common Values

ValueCountFrequency (%)
Flash Sale 205
20.5%
New Launch 204
20.4%
Influencer Collab 202
20.2%
Limited Edition 199
19.9%
Seasonal Sale 190
19.0%

Length

2025-04-28T22:59:59.616786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-28T22:59:59.875565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sale 395
19.8%
flash 205
10.2%
new 204
10.2%
launch 204
10.2%
influencer 202
10.1%
collab 202
10.1%
limited 199
10.0%
edition 199
10.0%
seasonal 190
9.5%

Most occurring characters

ValueCountFrequency (%)
l 1396
 
10.8%
e 1392
 
10.7%
a 1386
 
10.7%
1000
 
7.7%
n 997
 
7.7%
i 796
 
6.1%
o 591
 
4.6%
S 585
 
4.5%
h 409
 
3.2%
c 406
 
3.1%
Other values (15) 4021
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9979
76.9%
Uppercase Letter 2000
 
15.4%
Space Separator 1000
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 1396
14.0%
e 1392
13.9%
a 1386
13.9%
n 997
10.0%
i 796
8.0%
o 591
 
5.9%
h 409
 
4.1%
c 406
 
4.1%
u 406
 
4.1%
t 398
 
4.0%
Other values (7) 1802
18.1%
Uppercase Letter
ValueCountFrequency (%)
S 585
29.2%
L 403
20.2%
F 205
 
10.2%
N 204
 
10.2%
I 202
 
10.1%
C 202
 
10.1%
E 199
 
10.0%
Space Separator
ValueCountFrequency (%)
1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11979
92.3%
Common 1000
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 1396
 
11.7%
e 1392
 
11.6%
a 1386
 
11.6%
n 997
 
8.3%
i 796
 
6.6%
o 591
 
4.9%
S 585
 
4.9%
h 409
 
3.4%
c 406
 
3.4%
u 406
 
3.4%
Other values (14) 3615
30.2%
Common
ValueCountFrequency (%)
1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 1396
 
10.8%
e 1392
 
10.7%
a 1386
 
10.7%
1000
 
7.7%
n 997
 
7.7%
i 796
 
6.1%
o 591
 
4.6%
S 585
 
4.5%
h 409
 
3.2%
c 406
 
3.1%
Other values (15) 4021
31.0%

Impressions
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226973.76
Minimum10673
Maximum995224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-28T23:00:00.222292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10673
5-th percentile24762
Q195346
median174447
Q3260984.75
95-th percentile791357.3
Maximum995224
Range984551
Interquartile range (IQR)165638.75

Descriptive statistics

Standard deviation211379.76
Coefficient of variation (CV)0.93129601
Kurtosis3.5057455
Mean226973.76
Median Absolute Deviation (MAD)81758.5
Skewness1.9688757
Sum2.2697376 × 108
Variance4.4681402 × 1010
MonotonicityNot monotonic
2025-04-28T23:00:00.543708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122840 1
 
0.1%
116037 1
 
0.1%
102453 1
 
0.1%
57515 1
 
0.1%
25270 1
 
0.1%
230029 1
 
0.1%
71350 1
 
0.1%
446345 1
 
0.1%
157337 1
 
0.1%
199447 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
10673 1
0.1%
10883 1
0.1%
11138 1
0.1%
11480 1
0.1%
11548 1
0.1%
11634 1
0.1%
11667 1
0.1%
12111 1
0.1%
12329 1
0.1%
12602 1
0.1%
ValueCountFrequency (%)
995224 1
0.1%
992369 1
0.1%
990139 1
0.1%
983376 1
0.1%
982371 1
0.1%
979437 1
0.1%
977866 1
0.1%
964269 1
0.1%
959302 1
0.1%
958893 1
0.1%

Clicks
Real number (ℝ)

High correlation 

Distinct979
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12952.034
Minimum88
Maximum126573
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-28T23:00:00.858165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile953.95
Q13304
median8289.5
Q316286.5
95-th percentile40504.6
Maximum126573
Range126485
Interquartile range (IQR)12982.5

Descriptive statistics

Standard deviation16211.633
Coefficient of variation (CV)1.2516669
Kurtosis13.364744
Mean12952.034
Median Absolute Deviation (MAD)5456.5
Skewness3.2396242
Sum12952034
Variance2.6281704 × 108
MonotonicityNot monotonic
2025-04-28T23:00:01.208817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3270 2
 
0.2%
2893 2
 
0.2%
11543 2
 
0.2%
7338 2
 
0.2%
1848 2
 
0.2%
6267 2
 
0.2%
6373 2
 
0.2%
7940 2
 
0.2%
305 2
 
0.2%
508 2
 
0.2%
Other values (969) 980
98.0%
ValueCountFrequency (%)
88 1
0.1%
100 1
0.1%
102 1
0.1%
110 1
0.1%
176 1
0.1%
305 2
0.2%
308 1
0.1%
321 1
0.1%
363 1
0.1%
364 1
0.1%
ValueCountFrequency (%)
126573 1
0.1%
118069 1
0.1%
108926 1
0.1%
108261 1
0.1%
107766 1
0.1%
107228 1
0.1%
102882 1
0.1%
88563 1
0.1%
87599 1
0.1%
86086 1
0.1%

Conversions
Real number (ℝ)

High correlation 

Distinct866
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2247.554
Minimum10
Maximum25864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-28T23:00:01.531204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile140.9
Q1538
median1243.5
Q32597
95-th percentile7586.8
Maximum25864
Range25854
Interquartile range (IQR)2059

Descriptive statistics

Standard deviation3224.5952
Coefficient of variation (CV)1.4347131
Kurtosis18.539142
Mean2247.554
Median Absolute Deviation (MAD)881.5
Skewness3.8047674
Sum2247554
Variance10398014
MonotonicityNot monotonic
2025-04-28T23:00:01.876556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165 4
 
0.4%
318 4
 
0.4%
178 4
 
0.4%
485 4
 
0.4%
348 3
 
0.3%
102 3
 
0.3%
566 3
 
0.3%
289 3
 
0.3%
793 3
 
0.3%
155 3
 
0.3%
Other values (856) 966
96.6%
ValueCountFrequency (%)
10 1
0.1%
17 2
0.2%
20 2
0.2%
33 1
0.1%
37 2
0.2%
39 1
0.1%
47 1
0.1%
51 1
0.1%
55 1
0.1%
58 1
0.1%
ValueCountFrequency (%)
25864 1
0.1%
24829 1
0.1%
24423 1
0.1%
23673 1
0.1%
23160 1
0.1%
23087 1
0.1%
21745 1
0.1%
20230 1
0.1%
20060 1
0.1%
19362 1
0.1%

Cost
Real number (ℝ)

High correlation 

Distinct999
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38781.088
Minimum230.16
Maximum455746.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-28T23:00:02.192061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum230.16
5-th percentile2691.759
Q110307.683
median23859.34
Q346250.365
95-th percentile124907.25
Maximum455746.14
Range455515.98
Interquartile range (IQR)35942.683

Descriptive statistics

Standard deviation50694.081
Coefficient of variation (CV)1.3071856
Kurtosis17.010508
Mean38781.088
Median Absolute Deviation (MAD)15925.345
Skewness3.5632621
Sum38781088
Variance2.5698899 × 109
MonotonicityNot monotonic
2025-04-28T23:00:02.492903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22040.16 2
 
0.2%
11583.07 1
 
0.1%
2280.77 1
 
0.1%
22401.03 1
 
0.1%
35888.84 1
 
0.1%
18389.76 1
 
0.1%
11394.37 1
 
0.1%
44042.5 1
 
0.1%
25364.46 1
 
0.1%
21690.02 1
 
0.1%
Other values (989) 989
98.9%
ValueCountFrequency (%)
230.16 1
0.1%
238.26 1
0.1%
287.35 1
0.1%
376.5 1
0.1%
421.22 1
0.1%
691.73 1
0.1%
755.16 1
0.1%
767.98 1
0.1%
828.47 1
0.1%
833.67 1
0.1%
ValueCountFrequency (%)
455746.14 1
0.1%
426072.47 1
0.1%
336896.23 1
0.1%
332890.62 1
0.1%
331206.61 1
0.1%
325878.93 1
0.1%
312849.28 1
0.1%
311886.39 1
0.1%
298301.88 1
0.1%
284126.27 1
0.1%

Revenue
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean456665.85
Minimum2213.78
Maximum5517215.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-28T23:00:02.780709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2213.78
5-th percentile26930.785
Q1103295.68
median239855.96
Q3514645.86
95-th percentile1563744.5
Maximum5517215.3
Range5515001.6
Interquartile range (IQR)411350.19

Descriptive statistics

Standard deviation665951.17
Coefficient of variation (CV)1.4582898
Kurtosis18.097037
Mean456665.85
Median Absolute Deviation (MAD)171805.08
Skewness3.7667436
Sum4.5666585 × 108
Variance4.4349096 × 1011
MonotonicityNot monotonic
2025-04-28T23:00:03.399571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
285744.44 1
 
0.1%
128822.6 1
 
0.1%
334442.45 1
 
0.1%
179203.34 1
 
0.1%
38769.73 1
 
0.1%
206689.65 1
 
0.1%
88595 1
 
0.1%
67023.58 1
 
0.1%
121172.34 1
 
0.1%
255679.59 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
2213.78 1
0.1%
2714.13 1
0.1%
2885.26 1
0.1%
3658.35 1
0.1%
4508.71 1
0.1%
6933.6 1
0.1%
7641.99 1
0.1%
7965.59 1
0.1%
8543 1
0.1%
8557.69 1
0.1%
ValueCountFrequency (%)
5517215.34 1
0.1%
5004586.91 1
0.1%
4931152.09 1
0.1%
4924312 1
0.1%
4757261.76 1
0.1%
4530631.97 1
0.1%
4158418.43 1
0.1%
4129125.99 1
0.1%
4110033.93 1
0.1%
3786032.97 1
0.1%

CTR
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6954839
Minimum0.52733133
Maximum14.955942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-28T23:00:03.709702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.52733133
5-th percentile1.1807716
Q13.0264127
median5.2940613
Q37.6607775
95-th percentile11.922195
Maximum14.955942
Range14.428611
Interquartile range (IQR)4.6343648

Descriptive statistics

Standard deviation3.2921459
Coefficient of variation (CV)0.57802743
Kurtosis-0.20733975
Mean5.6954839
Median Absolute Deviation (MAD)2.3304638
Skewness0.60031165
Sum5695.4839
Variance10.838225
MonotonicityNot monotonic
2025-04-28T23:00:04.029547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.564148486 1
 
0.1%
2.910278618 1
 
0.1%
11.00211804 1
 
0.1%
11.06841693 1
 
0.1%
6.588840522 1
 
0.1%
5.215429359 1
 
0.1%
4.632095305 1
 
0.1%
1.439469469 1
 
0.1%
5.384620274 1
 
0.1%
5.068514442 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
0.5273313307 1
0.1%
0.5279806699 1
0.1%
0.562280024 1
0.1%
0.5838335964 1
0.1%
0.5845117379 1
0.1%
0.599119877 1
0.1%
0.6020780053 1
0.1%
0.6238062158 1
0.1%
0.6860062032 1
0.1%
0.6955109221 1
0.1%
ValueCountFrequency (%)
14.95594207 1
0.1%
14.9134352 1
0.1%
14.83841602 1
0.1%
14.79561177 1
0.1%
14.68888101 1
0.1%
14.67014411 1
0.1%
14.65622706 1
0.1%
14.65486726 1
0.1%
14.54439442 1
0.1%
14.50927176 1
0.1%

Conversion_Rate
Real number (ℝ)

High correlation 

Distinct999
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.348882
Minimum5.0249465
Maximum34.953941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-28T23:00:04.326784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.0249465
5-th percentile6.3969701
Q111.851664
median17.131639
Q322.107965
95-th percentile30.794748
Maximum34.953941
Range29.928994
Interquartile range (IQR)10.256301

Descriptive statistics

Standard deviation7.1002115
Coefficient of variation (CV)0.40926046
Kurtosis-0.48801584
Mean17.348882
Median Absolute Deviation (MAD)5.2087918
Skewness0.31606093
Sum17348.882
Variance50.413003
MonotonicityNot monotonic
2025-04-28T23:00:04.621752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.5 2
 
0.2%
8.214849921 1
 
0.1%
26.5103697 1
 
0.1%
6.574598678 1
 
0.1%
9.97193761 1
 
0.1%
19.2684711 1
 
0.1%
18.92212023 1
 
0.1%
18.48828957 1
 
0.1%
5.182879377 1
 
0.1%
19.96377157 1
 
0.1%
Other values (989) 989
98.9%
ValueCountFrequency (%)
5.024946543 1
0.1%
5.052051439 1
0.1%
5.057096248 1
0.1%
5.079681275 1
0.1%
5.086580087 1
0.1%
5.097554952 1
0.1%
5.120248254 1
0.1%
5.132288094 1
0.1%
5.142475512 1
0.1%
5.150241915 1
0.1%
ValueCountFrequency (%)
34.95394063 1
0.1%
34.90547264 1
0.1%
34.86560082 1
0.1%
34.86423515 1
0.1%
34.86234315 1
0.1%
34.60746637 1
0.1%
34.59395757 1
0.1%
34.59187343 1
0.1%
34.56577816 1
0.1%
34.36018957 1
0.1%

ROI
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1110.0491
Minimum119.17334
Maximum3358.4927
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-04-28T23:00:04.953728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum119.17334
5-th percentile297.15645
Q1652.69639
median1031.5873
Q31445.2184
95-th percentile2215.8507
Maximum3358.4927
Range3239.3193
Interquartile range (IQR)792.522

Descriptive statistics

Standard deviation594.84381
Coefficient of variation (CV)0.53587164
Kurtosis0.60568927
Mean1110.0491
Median Absolute Deviation (MAD)389.2273
Skewness0.83893951
Sum1110049.1
Variance353839.16
MonotonicityNot monotonic
2025-04-28T23:00:05.284780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1453.82365 1
 
0.1%
1030.581155 1
 
0.1%
659.3630016 1
 
0.1%
1049.190932 1
 
0.1%
484.1762034 1
 
0.1%
370.0808252 1
 
0.1%
678.8413949 1
 
0.1%
164.2420931 1
 
0.1%
440.9230736 1
 
0.1%
630.0980014 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
119.1733439 1
0.1%
141.4271705 1
0.1%
153.2213997 1
0.1%
155.1170612 1
0.1%
164.2420931 1
0.1%
183.0567436 1
0.1%
185.0538081 1
0.1%
190.7604207 1
0.1%
199.0095604 1
0.1%
200.2550242 1
0.1%
ValueCountFrequency (%)
3358.492671 1
0.1%
3287.61202 1
0.1%
3239.892853 1
0.1%
3166.873572 1
0.1%
3038.086042 1
0.1%
3010.981818 1
0.1%
3001.917012 1
0.1%
2976.340208 1
0.1%
2925.475027 1
0.1%
2896.132913 1
0.1%

Interactions

2025-04-28T22:59:53.753006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:40.011344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:41.866223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:43.937579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:45.901970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:47.769464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:49.666029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:51.557846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:54.013736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:40.230159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:42.101846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:44.171050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:46.141390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:48.019302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:49.913718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:51.809799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:54.272040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:40.478657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:42.305683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:44.404840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:46.362248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:48.274980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:50.144714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:52.063967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:54.518867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:40.696371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:42.539085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:44.648509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:46.599495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:48.511080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:50.386890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:52.551064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:54.746604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:40.896090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:42.760833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:44.894790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:46.818803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:48.696318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:50.607780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:52.781364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:54.999213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:41.125479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:42.970685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:45.156709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:47.040856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:48.922997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:50.841581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:53.022088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:55.252233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:41.387895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:43.220236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:45.434223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:47.292539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:49.180217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:51.081641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:53.285536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:55.503892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:41.608037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:43.657056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:45.646389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:47.525295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:49.421183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:51.322577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-28T22:59:53.516303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-28T23:00:05.540603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CTRCampaign_NameChannelClicksConversion_RateConversionsCostImpressionsProduct_CategoryPromo_TypeROIRevenue
CTR1.0000.0000.2840.592-0.0540.5350.5840.0080.0000.000-0.0250.528
Campaign_Name0.0001.0000.0000.2060.0230.1530.1880.2700.0000.0410.0350.146
Channel0.2840.0001.0000.0960.0490.0760.0800.0350.0440.0000.0000.084
Clicks0.5920.2060.0961.000-0.0350.9170.9820.7660.0000.0440.0060.910
Conversion_Rate-0.0540.0230.049-0.0351.0000.327-0.035-0.0050.3850.0330.8660.322
Conversions0.5350.1530.0760.9170.3271.0000.9030.7100.0850.0000.3240.992
Cost0.5840.1880.0800.982-0.0350.9031.0000.7510.0000.000-0.0580.896
Impressions0.0080.2700.0350.766-0.0050.7100.7511.0000.0000.0660.0240.707
Product_Category0.0000.0000.0440.0000.3850.0850.0000.0001.0000.0000.2810.106
Promo_Type0.0000.0410.0000.0440.0330.0000.0000.0660.0001.0000.0480.026
ROI-0.0250.0350.0000.0060.8660.324-0.0580.0240.2810.0481.0000.352
Revenue0.5280.1460.0840.9100.3220.9920.8960.7070.1060.0260.3521.000

Missing values

2025-04-28T22:59:55.823582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-28T22:59:56.223498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Campaign_IDCampaign_NameProduct_CategoryChannelPromo_TypeImpressionsClicksConversionsCostRevenueCTRConversion_RateROI
0AD001Yeezy CollectionSneakersFacebookSeasonal Sale116037337763911394.37128822.602.91027918.9221201030.581155
1AD002NMD DropAccessoriesInstagramInfluencer Collab10245311272208444042.50334442.4511.00211818.488290659.363002
2AD003NMD RestockSneakersEmailFlash Sale57515636675515593.87179203.3411.06841711.8598811049.190932
3AD004Stan Smith RestockApparelYouTubeSeasonal Sale2527016652336636.6538769.736.58884113.993994484.176203
4AD005Predator DropApparelYouTubeNew Launch23002911997131543968.96206689.655.21542910.961074370.080825
5AD006Yeezy RestockApparelYouTubeSeasonal Sale71350330555811375.2388595.004.63209516.883510678.841395
6AD007NMD LaunchAccessoriesTikTokFlash Sale446345642533325364.4667023.581.4394695.182879164.242093
7AD008Originals CollectionAccessoriesEmailLimited Edition10431715323152835888.84314364.3814.6888819.971938775.939094
8AD009Originals LaunchAccessoriesTikTokSeasonal Sale15583710489209421690.02482235.326.73075119.9637722123.305096
9AD010NMD EditionSneakersTikTokSeasonal Sale35756508641763.8914052.271.42074112.598425696.663624
Campaign_IDCampaign_NameProduct_CategoryChannelPromo_TypeImpressionsClicksConversionsCostRevenueCTRConversion_RateROI
990AD991Ultraboost DropSneakersTikTokFlash Sale2547967715230228628.37433208.133.02791329.8379781413.212698
991AD992Originals CollectionApparelYouTubeInfluencer Collab27204818212437750802.011087980.606.69440724.0336042041.609358
992AD993NMD RestockSneakersTikTokLimited Edition15295611709195338842.22437602.497.65514316.6794771026.615549
993AD994Predator EditionApparelYouTubeLimited Edition19826214261555426.5233019.180.71925010.869565508.477993
994AD995Stan Smith RestockSneakersTikTokLimited Edition698174474105515632.93188198.026.40818123.5806881103.856347
995AD996Yeezy DropSneakersEmailLimited Edition15919014097349645968.76536125.318.85545624.7996031066.281862
996AD997NMD RestockApparelInstagramNew Launch473328141022688.3220356.281.71976712.530713657.211939
997AD998Stan Smith RestockApparelInstagramSeasonal Sale2650378051126417856.72292253.813.03768915.6999131536.660092
998AD999Stan Smith LaunchAccessoriesInstagramInfluencer Collab592928362417339130111.571618112.556.11220920.2505451143.634636
999AD1000Originals CollectionSneakersTikTokFlash Sale1228406835131718389.76285744.445.56414819.2684711453.823650